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Course Outline
Introduction to Edge-Based Artificial Intelligence for Computer Vision
- Overview of edge computing capabilities and operational advantages
- Comparative analysis: Cloud-based versus edge-based AI architectures
- Primary challenges associated with real-time image processing
Deployment of Deep Learning Models on Edge Hardware
- Introduction to TensorFlow Lite and OpenVINO for government use cases
- Strategies for optimizing and quantizing models for edge deployment
- Case study: Implementation of YOLOv8 on edge hardware
Hardware Acceleration for Real-Time Inference Operations
- Overview of edge computing infrastructure (NVIDIA Jetson, Google Coral, FPGAs)
- Utilization of GPU and TPU acceleration technologies
- Methodologies for benchmarking and performance evaluation
Real-Time Object Detection and Tracking Systems
- Implementation of object detection frameworks using YOLO models
- Protocols for tracking dynamic objects in real-time environments
- Enhancement of detection accuracy through sensor fusion techniques
Optimization Strategies for Edge Artificial Intelligence
- Techniques for reducing model footprint via pruning and quantization
- Methods for minimizing latency and energy consumption
- Procedures for retraining and fine-tuning edge models
Integration of Edge AI with Internet of Things (IoT) Infrastructure
- Deployment of AI models on smart cameras and IoT endpoints for government operations
- Facilitating real-time decision-making at the edge
- Data exchange protocols between edge devices and centralized cloud systems
Security Protocols and Ethical Frameworks in Edge AI
- Addressing data privacy requirements in edge AI applications
- Measures to ensure model resilience against adversarial threats
- Adherence to regulatory standards and ethical AI principles
Summary and Future Directions
Requirements
- Working knowledge of computer vision principles
- Practical experience utilizing Python and deep learning frameworks
- Foundational understanding of edge computing architectures and IoT devices
Audience
- Computer vision engineers
- Artificial intelligence developers
- IoT specialists
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example